Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
Journal:
arXiv
Published Date:
Dec 30, 2024
Abstract
Computer vision technology, which involves analyzing images and videos
captured by cameras through deep learning algorithms, has significantly
advanced the field of human fall detection. This study focuses on the
application of the YoloV8 Nano model in identifying fall incidents within
passenger elevators, a context that presents unique challenges due to the
enclosed environment and varying lighting conditions. By training the model on
a robust dataset comprising over 10,000 images across diverse elevator types,
we aim to enhance the detection precision and recall rates. The model's
performance, with an 85% precision and 82% recall in fall detection,
underscores its potential for integration into existing elevator safety systems
to enable rapid intervention.
